In [1]}, the authors proposed a system that uses ML to automate the signing process of transactions as well as detection of anomaly transactions. If a transaction is detected as anomalous, the initiator of the transaction has to sign a transaction manually. In [2]}, the authors proposed a supervised ML-based fraud detection methodology for the transactions in the Ethereum blockchain. The features used in the method include average gas price, average gas limit, number of transactions, the value of transactions, and unique incoming/outgoing transactions. Among supervised ML algorithms, the authors found the Random Forest model to produce the best results. Besides these, there have been several ML-based methodologies that use features extracted from underlying temporal graphs to detect malicious accounts on Ethereum blockchain [3]}. Here, the authors introduced the concept of bursty behavior and change in the neighborhood as features. These new features are used in addition to the features already used in other state-of-the-art methodologies. The combining of temporal graph properties and supervised ML lead to the identification of previously undetected suspicious user accounts. In [3]}, the authors also survey different state-of-the-art approaches used within this frame. Based on their analysis, they propose time-variant probability scores following a sliding data window. Our reputation-based consensus method uses this feature for reputation values. A user's illicit behavior leads to its banishing from the consensus process for a predefined period. It locks a malicious user's account for the same period and provides an opportunity to LEAs for u=initiating action. In [5]}, the authors detect illicit activities such as gambling, phishing, and money laundering through the identification of patterns in cyclic money transfer. Besides research-based methodologies, several companies, such as Chainalysis [6]} offer services to the LEAs by analyzing the transaction data of several cryptocurrency-based blockchains.
